SOTAVerified

Dimensionality Reduction

Dimensionality reduction is the task of reducing the dimensionality of a dataset.

( Image credit: openTSNE )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1351–1375 of 3304 papers

TitleStatusHype
Consistent Estimation of Low-Dimensional Latent Structure in High-Dimensional Data—0
Agriculture Commodity Arrival Prediction using Remote Sensing Data: Insights and Beyond—0
From Manifold to Manifold: Geometry-Aware Dimensionality Reduction for SPD Matrices—0
From Players to Champions: A Generalizable Machine Learning Approach for Match Outcome Prediction with Insights from the FIFA World Cup—0
Generalized BackPropagation, Étude De Cas: Orthogonality—0
A Powerful Face Preprocessing For Robust Kinship Verification based Tensor Analyses—0
From RGB to Spectrum for Natural Scenes via Manifold-Based Mapping—0
Construction of neural networks for realization of localized deep learning—0
From which world is your graph?—0
From which world is your graph—0
Full-dimensional characterisation of time-warped spike-time stimulus-response distribution geometries—0
Contextual Bandits with Sparse Data in Web setting—0
Generalized two-dimensional linear discriminant analysis with regularization—0
Geometric Machine Learning on EEG Signals—0
Contextual Categorization Enhancement through LLMs Latent-Space—0
Functional Inverse Regression in an Enlarged Dimension Reduction Space—0
Efficiently Computing Similarities to Private Datasets—0
Functional sufficient dimension reduction through information maximization with application to classification—0
Contrasting Syntagmatic and Paradigmatic Relations: Insights from Distributional Semantic Models—0
Functorial Manifold Learning—0
Efficient Learning and Planning with Compressed Predictive States—0
Application of Deep Learning for Predictive Maintenance of Oilfield Equipment—0
Fusing Vector Space Models for Domain-Specific Applications—0
Fusion of heterogeneous bands and kernels in hyperspectral image processing—0
Bridging Fairness and Environmental Sustainability in Natural Language Processing—0
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All datasetsMCAEMNIST

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy90.9—Unverified
2tSNEClassification Accuracy51.5—Unverified
3IVISClassification Accuracy46.6—Unverified
4UMAPClassification Accuracy41.3—Unverified
#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy71.1—Unverified
2QSClassification Accuracy68—Unverified